Recent years have seen a growing interest in methods for predicting a
variable of interest, such as a subject's diagnosis, from medical images.
Methods based on discriminative modeling excel at making accurate predictions,
but are challenged in their ability to explain their decisions in anatomically
meaningful terms. In this paper, we propose a simple technique for
single-subject prediction that is inherently interpretable. It augments the
generative models used in classical human brain mapping techniques, in which
cause-effect relations can be encoded, with a multivariate noise model that
captures dominant spatial correlations. Experiments demonstrate that the
resulting model can be efficiently inverted to make accurate subject-level
predictions, while at the same time offering intuitive causal explanations of
its inner workings. The method is easy to use: training is fast for typical
training set sizes, and only a single hyperparameter needs to be set by the
user. Our code is available at
this https URL

本论文提出了一种多变量噪声模型与神经元影像生成模型的简单技术，通过构建因果关系实现了单个主体预测及对预测结果的解释，且在训练模型时只需要一个超参数。

面向可解释性主体级预测的轻量级因果模型

A Lightweight Causal Model for Interpretable Subject-level Prediction

We address weakly supervised action alignment and segmentation in videos,
where only the order of occurring actions is available during training. We
propose Discriminative Differentiable Dynamic Time Warping (D3TW), the first
discriminative model using weak ordering supervision. The key technical
challenge for discriminative modeling with weak supervision is that the loss
function of the ordering supervision is usually formulated using dynamic
programming and is thus not differentiable. We address this challenge with a
continuous relaxation of the min-operator in dynamic programming and extend the
alignment loss to be differentiable. The proposed D3TW innovatively solves
sequence alignment with discriminative modeling and end-to-end training, which
substantially improves the performance in weakly supervised action alignment
and segmentation tasks. We show that our model is able to bypass the
degenerated sequence problem usually encountered in previous work and
outperform the current state-of-the-art across three evaluation metrics in two
challenging datasets.

本文提出了一种名为 D3TW 的新模型，通过创新性的弱监督序列排序方式，在视频中实现了弱监督行为对齐和分割，并在数据集上进行了测试。

D3TW: 区分性可微分动态时间规整方法 用于弱监督动作对齐和分割

D3TW: Discriminative Differentiable Dynamic Time Warping for Weakly  Supervised Action Alignment and Segmentation

Deep reinforcement learning algorithms have been shown to learn complex tasks
using highly general policy classes. However, sparse reward problems remain a
significant challenge. Exploration methods based on novelty detection have been
particularly successful in such settings but typically require generative or
predictive models of the observations, which can be difficult to train when the
observations are very high-dimensional and complex, as in the case of raw
images. We propose a novelty detection algorithm for exploration that is based
entirely on discriminatively trained exemplar models, where classifiers are
trained to discriminate each visited state against all others. Intuitively,
novel states are easier to distinguish against other states seen during
training. We show that this kind of discriminative modeling corresponds to
implicit density estimation, and that it can be combined with count-based
exploration to produce competitive results on a range of popular benchmark
tasks, including state-of-the-art results on challenging egocentric
observations in the vizDoom benchmark.

本文提出了一种基于判别式训练范例模型的探索性新颖性检测算法，该算法可以应用于稀疏回报问题的深度强化学习任务，此方法在 vizDoom 基准测试中取得了最先进的结果。